It is no secret that talent acquisition has been way behind in gathering and using data. While many recruiters sit on top of huge amounts of candidate data and could access additional data from the corporate HRIS, they rarely do or even know how to access it. They do not rely on data to screen or assess candidates, nor do they objectively define the quality of hire. They still report activity metrics rather than value metrics based on data.
This quote from Sudheesh Nair, CEO of ThoughSpot, about data and the skill to use it is right on the money:
“Very soon, if you’re unable to marry hard data with business context to define and execute a strategy, you’re going to struggle in the workplace. The ideal candidate for businesses in 2021 and beyond will be a person who can both understand and speak data — because in a few short years, data literacy will be something employers demand and expect. Those who want to get ahead are acquiring these talents now.”
The profession has relied on a host of assumptions, stereotypes, anecdotes, and beliefs. This is a small sampling of common ones.
The university or school that a candidate graduated from equates somehow to quality.
GPA is important to judge candidate quality.
Experience is a good indicator of performance on the job.
Interviews are as valid or more valid than tests.
Culture fit (i.e., personality) is very important.
Quality of hire is determined by manager satisfaction and retention.
Older candidates are usually less desirable than younger ones (but we never admit this.)
All of these have long ago been discredited yet are still in use. Data could help rid us of these and compel recruiters and hiring managers to focus on specific skills rather than bais, history, and gut feel. Here are a few areas where data and A.I. could prove useful.
#1. Reducing the number of unqualified candidates that apply for positions by better matching candidates to job requirements.
#2. Allowing recruiters to present fewer candidates by assessing and ranking applicants based on specific skills, personality, culture fit, or ability.
#3. Eliminating overt and hidden bias and ensuring that selection criteria are objective.
#4. Defining the quality of a candidate and a new hire objectively.
Artificial intelligence is poised to help. Data scientists are now using Artificial Narrow Intelligence (Narrow AI) to provide more calibrated and valid results. IBM and Google use Narrow AI to win at chess and Go and to teach self-driving cars. Narrow AI learns about and becomes an expert in a single area, such as recruiting. With assessment and matching tools using Narrow AI, recruiters can dig into employee data and discover employees’ success characteristics. It can recommend candidates based on skills and ability, eliminating bias and assumptions. Coupled to algorithms, it can offer answers to questions like these:
When is a passive candidate the most likely to be receptive to a call?
Where should we post this job for maximum effect?
Is candidate A better suited for this position than Candidate B?
Which of these skills are most important for success in this position?
What skills are most used within this function?
While this may sound fanciful or overly optimistic, A.I. can achieve these. Tools such as chatbots and online screening and assessment tools are available and valid.
If you want to remain successful in talent acquisition, you will need to become data literate and learn data analysis basics. Communicating in the language of data will make your conversations with hiring managers and leadership more effective and harder to dispute or disagree with.
It would help if you also understood, at least conceptually, how A.I. and algorithms work. There are a host of resources available to help. I have listed a few favorite books as well as certificate courses. Make time to learn and develop the ability to access the data you have and to make sense of it. Also, learning the basics of how A.I. and algorithms work will help you understand their strengths and limitations.
The future will call upon us to do things very differently. Seat-of-the-pants decisions will have to become data-driven decisions, and candidates and hiring managers will demand answers and facts, not opinions or feelings.
The Data-Driven and Digital HR Business Partner (Dave Ulrich)
Data Analytics from Cornell (certificate program)
Websites such as Social Talent offer recruiter specific training as well.
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